{"title":"结合共现图的动态规划越南语文本重音恢复新方法","authors":"Ho Trong Nghia, Do Phuc","doi":"10.1109/RIVF.2009.5174609","DOIUrl":null,"url":null,"abstract":"In this paper, we would like to introduce a new approach to recover Vietnamese text's accents. Given a Vietnamese text in which accents are lost, our goal is to seek for a recovered text that yields a best lexical probability. Using a dynamic programming approach, we first build a model of language for Vietnamese as a lexical database which gives lexical probabilities to Vietnamese sentences. Second, we construct a map of literal translations of Vietnamese words to restrict our searching space. Finally, we apply dynamic programming as a searching engine to seek out the most probable sentence. We also use the co-occurrence graph to increase the accuracy of selection, the experimental results show that the average accuracy of our approach is about 93%-94%.","PeriodicalId":243397,"journal":{"name":"2009 IEEE-RIVF International Conference on Computing and Communication Technologies","volume":"538 ","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"A New Approach To Accent Restoration Of Vietnamese Texts Using Dynamic Programming Combined With Co-Occurrence Graph\",\"authors\":\"Ho Trong Nghia, Do Phuc\",\"doi\":\"10.1109/RIVF.2009.5174609\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we would like to introduce a new approach to recover Vietnamese text's accents. Given a Vietnamese text in which accents are lost, our goal is to seek for a recovered text that yields a best lexical probability. Using a dynamic programming approach, we first build a model of language for Vietnamese as a lexical database which gives lexical probabilities to Vietnamese sentences. Second, we construct a map of literal translations of Vietnamese words to restrict our searching space. Finally, we apply dynamic programming as a searching engine to seek out the most probable sentence. We also use the co-occurrence graph to increase the accuracy of selection, the experimental results show that the average accuracy of our approach is about 93%-94%.\",\"PeriodicalId\":243397,\"journal\":{\"name\":\"2009 IEEE-RIVF International Conference on Computing and Communication Technologies\",\"volume\":\"538 \",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE-RIVF International Conference on Computing and Communication Technologies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RIVF.2009.5174609\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE-RIVF International Conference on Computing and Communication Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RIVF.2009.5174609","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A New Approach To Accent Restoration Of Vietnamese Texts Using Dynamic Programming Combined With Co-Occurrence Graph
In this paper, we would like to introduce a new approach to recover Vietnamese text's accents. Given a Vietnamese text in which accents are lost, our goal is to seek for a recovered text that yields a best lexical probability. Using a dynamic programming approach, we first build a model of language for Vietnamese as a lexical database which gives lexical probabilities to Vietnamese sentences. Second, we construct a map of literal translations of Vietnamese words to restrict our searching space. Finally, we apply dynamic programming as a searching engine to seek out the most probable sentence. We also use the co-occurrence graph to increase the accuracy of selection, the experimental results show that the average accuracy of our approach is about 93%-94%.